OpenRaiser/Intern-Atlas huggingface dataset:
Understanding Questions that Arise When Working with Business Documents
Venue: arXiv cs.HC 2022
Year: 2022

While digital assistants are increasingly used to help with various productivity tasks, less attention has been paid to employing them in the domain of business documents. To build an agent that can handle users' information needs in this domain, we must first understand the types of assistance that users desire when working on their documents. In this work, we present results from two user studies that characterize the information needs and queries of authors, reviewers, and readers of business documents. In the first study, we used experience sampling to collect users' questions in-situ as they were working with their documents, and in the second, we built a human-in-the-loop document Q&A system which rendered assistance with a variety of users' questions. Our results have implications for the design of document assistants that complement AI with human intelligence including whether particular skillsets or roles within the document are needed from human respondents, as well as the challenges around such systems.
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network
Venue: arXiv cs.CV 2022
Year: 2022

Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large field of view, i.e more contexual information. This can be achieved through analyzing the digital slides at lower resolution. A potential drawback is missing out on details present at a higher resolution. To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Pyramidal Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal structure through co-training of proposed Context and Detail Modules, which operate on inputs from multiple resolutions. The residual connections between the modules enable the joint training paradigm while learning self-supervised representation for WSIs. The efficacy of CD-Net is demonstrated in classifying Lung Adenocarcinoma from Squamous cell carcinoma.
Investigation of Different Calibration Methods for Deep Speaker Embedding based Verification Systems
Venue: arXiv cs.SD 2022
Year: 2022

Deep speaker embedding extractors have already become new state-of-the-art systems in the speaker verification field. However, the problem of verification score calibration for such systems often remains out of focus. An irrelevant score calibration leads to serious issues, especially in the case of unknown acoustic conditions, even if we use a strong speaker verification system in terms of threshold-free metrics. This paper presents an investigation over several methods of score calibration: a classical approach based on the logistic regression model; the recently presented magnitude estimation network MagnetO that uses activations from the pooling layer of the trained deep speaker extractor and generalization of such approach based on separate scale and offset prediction neural networks. An additional focus of this research is to estimate the impact of score normalization on the calibration performance of the system. The obtained results demonstrate that there are no serious problems if in-domain development data are used for calibration tuning. Otherwise, a trade-off between good calibration performance and threshold-free system quality arises. In most cases using adaptive s-norm helps to stabilize score distributions and to improve system performance. Meanwhile, some experiments demonstrate that novel approaches have their limits in score stabilization on several datasets.
Toward Deep Learning Based Access Control
Venue: arXiv cs.CR 2022
Year: 2022

A common trait of current access control approaches is the challenging need to engineer abstract and intuitive access control models. This entails designing access control information in the form of roles (RBAC), attributes (ABAC), or relationships (ReBAC) as the case may be, and subsequently, designing access control rules. This framework has its benefits but has significant limitations in the context of modern systems that are dynamic, complex, and large-scale, due to which it is difficult to maintain an accurate access control state in the system for a human administrator. This paper proposes Deep Learning Based Access Control (DLBAC) by leveraging significant advances in deep learning technology as a potential solution to this problem. We envision that DLBAC could complement and, in the long-term, has the potential to even replace, classical access control models with a neural network that reduces the burden of access control model engineering and updates. Without loss of generality, we conduct a thorough investigation of a candidate DLBAC model, called DLBAC_alpha, using both real-world and synthetic datasets. We demonstrate the feasibility of the proposed approach by addressing issues related to accuracy, generalization, and explainability. We also discuss challenges and future research directions.
A machine learning-based severity prediction tool for diabetic sensorimotor polyneuropathy using Michigan neuropathy screening instrumentations
Venue: arXiv cs.LG 2022
Year: 2022

Background: Diabetic Sensorimotor polyneuropathy (DSPN) is a major long-term complication in diabetic patients associated with painful neuropathy, foot ulceration and amputation. The Michigan neuropathy screening instrument (MNSI) is one of the most common screening techniques for DSPN, however, it does not provide any direct severity grading system. Method: For designing and modelling the DSPN severity grading systems for MNSI, 19 years of data from Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials were used. MNSI variables and patient outcomes were investigated using machine learning tools to identify the features having higher association in DSPN identification. A multivariable logistic regression-based nomogram was generated and validated for DSPN severity grading. Results: The top-7 ranked features from MNSI: 10-gm filament, Vibration perception (R), Vibration perception (L), previous diabetic neuropathy, the appearance of deformities, appearance of callus and appearance of fissure were identified as key features for identifying DSPN using the extra tree model. The area under the curve (AUC) of the nomogram for the internal and external datasets were 0.9421 and 0.946, respectively. From the developed nomogram, the probability of having DSPN was predicted and a DSPN severity scoring system for MNSI was developed from the probability score. The model performance was validated on an independent dataset. Patients were stratified into four severity levels: absent, mild, moderate, and severe using a cut-off value of 10.5, 12.7 and 15 for a DSPN probability less than 50%, 75% to 90%, and above 90%, respectively. Conclusions: This study provides a simple, easy-to-use and reliable algorithm for defining the prognosis and management of patients with DSPN.
Autonomous Road Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds
Venue: arXiv cs.RO 2022
Year: 2022

An Autonomous Road Vehicle (ARV) can navigate various types of road networks using inputs such as throttle (acceleration), braking (deceleration), and steering (change of lateral direction). In most ARV driving scenarios that involve normal vehicle traffic and encounters with vulnerable road users (VRUs), ARVs are not required to take evasive action. This paper presents a novel Emergency Obstacle Avoidance Maneuver (EOAM) methodology for ARVs traveling at higher speeds and lower road surface friction, involving time-critical maneuver determination and control. The proposed EOAM Framework offers usage of the ARV's sensing, perception, control, and actuation system abilities as one cohesive system, to accomplish avoidance of an on-road obstacle, based first on performance feasibility and second on passenger comfort, and is designed to be well-integrated within an ARV high-level system. Co-simulation including the ARV EOAM logic in Simulink and a vehicle model in CarSim is conducted with speeds ranging from 55 to 165 km/h and on road surfaces with friction ranging from 1.0 to 0.1. The results are analyzed and given in the context of an entire ARV system, with implications for future work.
Assessing Evolutionary Terrain Generation Methods for Curriculum Reinforcement Learning
Venue: arXiv cs.NE 2022
Year: 2022

Curriculum learning allows complex tasks to be mastered via incremental progression over `stepping stone' goals towards a final desired behaviour. Typical implementations learn locomotion policies for challenging environments through gradual complexification of a terrain mesh generated through a parameterised noise function. To date, researchers have predominantly generated terrains from a limited range of noise functions, and the effect of the generator on the learning process is underrepresented in the literature. We compare popular noise-based terrain generators to two indirect encodings, CPPN and GAN. To allow direct comparison between both direct and indirect representations, we assess the impact of a range of representation-agnostic MAP-Elites feature descriptors that compute metrics directly from the generated terrain meshes. Next, performance and coverage are assessed when training a humanoid robot in a physics simulator using the PPO algorithm. Results describe key differences between the generators that inform their use in curriculum learning, and present a range of useful feature descriptors for uptake by the community.
Are Edge Weights in Summary Graphs Useful? -- A Comparative Study
Venue: arXiv cs.DB 2022
Year: 2022

Which one is better between two representative graph summarization models with and without edge weights? From web graphs to online social networks, large graphs are everywhere. Graph summarization, which is an effective graph compression technique, aims to find a compact summary graph that accurately represents a given large graph. Two versions of the problem, where one allows edge weights in summary graphs and the other does not, have been studied in parallel without direct comparison between their underlying representation models. In this work, we conduct a systematic comparison by extending three search algorithms to both models and evaluating their outputs on eight datasets in five aspects: (a) reconstruction error, (b) error in node importance, (c) error in node proximity, (d) the size of reconstructed graphs, and (e) compression ratios. Surprisingly, using unweighted summary graphs leads to outputs significantly better in all the aspects than using weighted ones, and this finding is supported theoretically. Notably, we show that a state-of-the-art algorithm can be improved substantially (specifically, 8.2X, 7.8X, and 5.9X in terms of (a), (b), and (c), respectively, when (e) is fixed) based on the observation.
Spatial-Aware Local Community Detection Guided by Dominance Relation
Venue: arXiv cs.SI 2022
Year: 2022

The problem of finding the spatial-aware community for a given node has been defined and investigated in geo-social networks. However, existing studies suffer from two limitations: a) the criteria of defining communities are determined by parameters, which are difficult to set; b) algorithms may require global information and are not suitable for situations where the network is incomplete. Therefore, we propose spatial-aware local community detection (SLCD), which finds the spatial-aware local community with only local information and defines the community based on the difference in the sparseness of edges inside and outside the community. Specifically, to address the SLCD problem, we design a novel spatial aware local community detection algorithm based on dominance relation, but this algorithm incurs high cost. To further improve the efficiency, we propose an approximate algorithm. Experimental results demonstrate that the proposed approximate algorithm outperforms the comparison algorithms.
SHOP: A Deep Learning Based Pipeline for near Real-Time Detection of Small Handheld Objects Present in Blurry Video
Venue: arXiv cs.CV 2022
Year: 2022

While prior works have investigated and developed computational models capable of object detection, models still struggle to reliably interpret images with motion blur and small objects. Moreover, none of these models are specifically designed for handheld object detection. In this work, we present SHOP (Small Handheld Object Pipeline), a pipeline that reliably and efficiently interprets blurry images containing handheld objects. The specific models used in each stage of the pipeline are flexible and can be changed based on performance requirements. First, images are deblurred and then run through a pose detection system where areas-of-interest are proposed around the hands of any people present. Next, object detection is performed on the images by a single-stage object detector. Finally, the proposed areas-of-interest are used to filter out low confidence detections. Testing on a handheld subset of Microsoft Common Objects in Context (MS COCO) demonstrates that this 3 stage process results in a 70 percent decrease in false positives while only reducing true positives by 17 percent in its strongest configuration. We also present a subset of MS COCO consisting solely of handheld objects that can be used to continue the development of handheld object detection methods. https://github.com/spider-sense/SHOP
Edge Detection and Deep Learning Based SETI Signal Classification Method
Venue: arXiv cs.CV 2022
Year: 2022

Scientists at the Berkeley SETI Research Center are Searching for Extraterrestrial Intelligence (SETI) by a new signal detection method that converts radio signals into spectrograms through Fourier transforms and classifies signals represented by two-dimensional time-frequency spectrums, which successfully converts a signal classification problem into an image classification task. In view of the negative impact of background noises on the accuracy of spectrograms classification, a new method is introduced in this paper. After Gaussian convolution smoothing the signals, edge detection functions are applied to detect the edge of the signals and enhance the outline of the signals, then the processed spectrograms are used to train the deep neural network to compare the classification accuracy of various image classification networks. The results show that the proposed method can effectively improve the classification accuracy of SETI spectrums.
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Automatic surgical scene segmentation is fundamental for facilitating cognitive intelligence in the modern operating theatre. Previous works rely on conventional aggregation modules (e.g., dilated convolution, convolutional LSTM), which only make use of the local context. In this paper, we propose a novel framework STswinCL that explores the complementary intra- and inter-video relations to boost segmentation performance, by progressively capturing the global context. We firstly develop a hierarchy Transformer to capture intra-video relation that includes richer spatial and temporal cues from neighbor pixels and previous frames. A joint space-time window shift scheme is proposed to efficiently aggregate these two cues into each pixel embedding. Then, we explore inter-video relation via pixel-to-pixel contrastive learning, which well structures the global embedding space. A multi-source contrast training objective is developed to group the pixel embeddings across videos with the ground-truth guidance, which is crucial for learning the global property of the whole data. We extensively validate our approach on two public surgical video benchmarks, including EndoVis18 Challenge and CaDIS dataset. Experimental results demonstrate the promising performance of our method, which consistently exceeds previous state-of-the-art approaches. Code is available at https://github.com/YuemingJin/STswinCL.
Identification and classification of exfoliated graphene flakes from microscopy images using a hierarchical deep convolutional neural network
Venue: arXiv cs.CV 2022
Year: 2022

Identification of the mechanically exfoliated graphene flakes and classification of the thickness is important in the nanomanufacturing of next-generation materials and devices that overcome the bottleneck of Moore's Law. Currently, identification and classification of exfoliated graphene flakes are conducted by human via inspecting the optical microscope images. The existing state-of-the-art automatic identification by machine learning is not able to accommodate images with different backgrounds while different backgrounds are unavoidable in experiments. This paper presents a deep learning method to automatically identify and classify the thickness of exfoliated graphene flakes on Si/SiO2 substrates from optical microscope images with various settings and background colors. The presented method uses a hierarchical deep convolutional neural network that is capable of learning new images while preserving the knowledge from previous images. The deep learning model was trained and used to classify exfoliated graphene flakes into monolayer (1L), bi-layer (2L), tri-layer (3L), four-to-six-layer (4-6L), seven-to-ten-layer (7-10L), and bulk categories. Compared with existing machine learning methods, the presented method possesses high accuracy and efficiency as well as robustness to the backgrounds and resolutions of images. The results indicated that our deep learning model has accuracy as high as 99% in identifying and classifying exfoliated graphene flakes. This research will shed light on scaled-up manufacturing and characterization of graphene for advanced materials and devices.
NeuraGen-A Low-Resource Neural Network based approach for Gender Classification
Venue: arXiv cs.SD 2022
Year: 2022

Human voice is the source of several important information. This is in the form of features. These Features help in interpreting various features associated with the speaker and speech. The speaker dependent work researchersare targeted towards speaker identification, Speaker verification, speaker biometric, forensics using feature, and cross-modal matching via speech and face images. In such context research, it is a very difficult task to come across clean, and well annotated publicly available speech corpus as data set. Acquiring volunteers to generate such dataset is also very expensive, not to mention the enormous amount of effort and time researchers spend to gather such data. The present paper work, a Neural Network proposal as NeuraGen focused which is a low-resource ANN architecture. The proposed tool used to classify gender of the speaker from the speech recordings. We have used speech recordings collected from the ELSDSR and limited TIMIT datasets, from which we extracted 8 speech features, which were pre-processed and then fed into NeuraGen to identify the gender. NeuraGen has successfully achieved accuracy of 90.7407% and F1 score of 91.227% in train and 20-fold cross validation dataset.
A Wavelet, AR and SVM based hybrid method for short-term wind speed prediction
Venue: arXiv cs.LG 2022
Year: 2022

Wind speed modelling and prediction has been gaining importance because of its significant roles in various stages of wind energy management. In this paper, we propose a hybrid model, based on wavelet transform to improve the accuracy of the short-term forecast. The wind speed time series are split into various frequency components using wavelet decomposition technique, and each frequency components are modelled separately. Since the components associated with the high- frequency range shows stochastic nature, we modelled them with autoregressive (AR) method and rest of low-frequency components modelled with support vector machine (SVM). The results of the hybrid method show a promising improvement in accuracy of wind speed prediction compared to that of stand-alone AR or SVM model.
Hybrid Routing Transformer for Zero-Shot Learning
Venue: arXiv cs.CV 2022
Year: 2022

Zero-shot learning (ZSL) aims to learn models that can recognize unseen image semantics based on the training of data with seen semantics. Recent studies either leverage the global image features or mine discriminative local patch features to associate the extracted visual features to the semantic attributes. However, due to the lack of the necessary top-down guidance and semantic alignment for ensuring the model attending to the real attribute-correlation regions, these methods still encounter a significant semantic gap between the visual modality and the attribute modality, which makes their prediction on unseen semantics unreliable. To solve this problem, this paper establishes a novel transformer encoder-decoder model, called hybrid routing transformer (HRT). In HRT encoder, we embed an active attention, which is constructed by both the bottom-up and the top-down dynamic routing pathways to generate the attribute-aligned visual feature. While in HRT decoder, we use static routing to calculate the correlation among the attribute-aligned visual features, the corresponding attribute semantics, and the class attribute vectors to generate the final class label predictions. This design makes the presented transformer model a hybrid of 1) top-down and bottom-up attention pathways and 2) dynamic and static routing pathways. Comprehensive experiments on three widely-used benchmark datasets, namely CUB, SUN, and AWA2, are conducted. The obtained experimental results demonstrate the effectiveness of the proposed method.
On Reinforcement Learning, Effect Handlers, and the State Monad
Venue: arXiv cs.PL 2022
Year: 2022

We study the algebraic effects and handlers as a way to support decision-making abstractions in functional programs, whereas a user can ask a learning algorithm to resolve choices without implementing the underlying selection mechanism, and give a feedback by way of rewards. Differently from some recently proposed approach to the problem based on the selection monad [Abadi and Plotkin, LICS 2021], we express the underlying intelligence as a reinforcement learning algorithm implemented as a set of handlers for some of these algebraic operations, including those for choices and rewards. We show how we can in practice use algebraic operations and handlers -- as available in the programming language EFF -- to clearly separate the learning algorithm from its environment, thus allowing for a good level of modularity. We then show how the host language can be taken as a lambda-calculus with handlers, this way showing what the essential linguistic features are. We conclude by hinting at how type and effect systems could ensure safety properties, at the same time pointing at some directions for further work.
Impact of Network Densification on the Performance of a Non-Public URLLC Factory Network
Venue: arXiv cs.NI 2022
Year: 2022

Densification of the network deployment, for example by adding new sectors or sites within an existing mobile communication network, has traditionally been an efficient way to improve the system coverage and capacity. That will be the case even for the ultra-reliable low-latency communication (URLLC) services, but the overall situation and the feasibility of the different deployment options will depend on the characteristics of the URLLC service in question. URLLC services with relaxed latency requirements, allowing multiple transmission attempts, can tolerate a decent level of inter-cell interference while still being able to guarantee the desired quality-of-service for all users, which makes it possible to improve the URLLC service capacity by adding new gNodeBs with omnidirectional or directional antennas. However, when the URLLC services require extremely low latency that do not allow for retransmissions, the system performance becomes quite sensitive to the inter-cell interference, which means that in practice the gNodeBs should be equipped with beamformed antennas to reach high levels of URLLC service capacity. Finally, an active distributed antenna system could be an efficient way to secure good coverage throughout the desired service area.